{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import autoencoder\n",
    "import model\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Feed-forward Auto-Encoder Neural Network  to reduce dimension + Deep Recurrent Neural Network + ARIMA + Extreme Boosting Gradient Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our target is Close market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "google = pd.read_csv('GOOG.csv')\n",
    "eur_myr = pd.read_csv('eur-myr.csv')\n",
    "usd_myr = pd.read_csv('usd-myr.csv')\n",
    "oil = pd.read_csv('oil.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "google['oil_price'] = oil['Price']\n",
    "google['oil_open'] = oil['Open']\n",
    "google['oil_high'] = oil['High']\n",
    "google['oil_low'] = oil['Low']\n",
    "google['eur_myr'] = eur_myr['Unnamed: 1']\n",
    "google['usd_myr'] = usd_myr['Unnamed: 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>oil_price</th>\n",
       "      <th>oil_open</th>\n",
       "      <th>oil_high</th>\n",
       "      <th>oil_low</th>\n",
       "      <th>eur_myr</th>\n",
       "      <th>usd_myr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>959.979980</td>\n",
       "      <td>962.539978</td>\n",
       "      <td>947.840027</td>\n",
       "      <td>953.270020</td>\n",
       "      <td>953.270020</td>\n",
       "      <td>1283400</td>\n",
       "      <td>54.27</td>\n",
       "      <td>54.26</td>\n",
       "      <td>54.39</td>\n",
       "      <td>54.22</td>\n",
       "      <td>4.9260</td>\n",
       "      <td>4.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-03</td>\n",
       "      <td>954.000000</td>\n",
       "      <td>958.000000</td>\n",
       "      <td>949.140015</td>\n",
       "      <td>957.789978</td>\n",
       "      <td>957.789978</td>\n",
       "      <td>888300</td>\n",
       "      <td>54.24</td>\n",
       "      <td>54.59</td>\n",
       "      <td>55.22</td>\n",
       "      <td>53.89</td>\n",
       "      <td>4.9232</td>\n",
       "      <td>4.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-04</td>\n",
       "      <td>957.000000</td>\n",
       "      <td>960.390015</td>\n",
       "      <td>950.690002</td>\n",
       "      <td>951.679993</td>\n",
       "      <td>951.679993</td>\n",
       "      <td>952400</td>\n",
       "      <td>54.38</td>\n",
       "      <td>54.08</td>\n",
       "      <td>54.85</td>\n",
       "      <td>53.93</td>\n",
       "      <td>4.9255</td>\n",
       "      <td>4.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-05</td>\n",
       "      <td>955.489990</td>\n",
       "      <td>970.909973</td>\n",
       "      <td>955.179993</td>\n",
       "      <td>969.960022</td>\n",
       "      <td>969.960022</td>\n",
       "      <td>1213800</td>\n",
       "      <td>54.15</td>\n",
       "      <td>54.16</td>\n",
       "      <td>54.46</td>\n",
       "      <td>53.75</td>\n",
       "      <td>4.9239</td>\n",
       "      <td>4.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>966.700012</td>\n",
       "      <td>979.460022</td>\n",
       "      <td>963.359985</td>\n",
       "      <td>978.890015</td>\n",
       "      <td>978.890015</td>\n",
       "      <td>1173900</td>\n",
       "      <td>53.90</td>\n",
       "      <td>52.80</td>\n",
       "      <td>54.20</td>\n",
       "      <td>52.25</td>\n",
       "      <td>4.9251</td>\n",
       "      <td>4.241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2017-10-02  959.979980  962.539978  947.840027  953.270020  953.270020   \n",
       "1  2017-10-03  954.000000  958.000000  949.140015  957.789978  957.789978   \n",
       "2  2017-10-04  957.000000  960.390015  950.690002  951.679993  951.679993   \n",
       "3  2017-10-05  955.489990  970.909973  955.179993  969.960022  969.960022   \n",
       "4  2017-10-06  966.700012  979.460022  963.359985  978.890015  978.890015   \n",
       "\n",
       "    Volume oil_price oil_open oil_high oil_low  eur_myr  usd_myr  \n",
       "0  1283400     54.27    54.26    54.39   54.22   4.9260    4.226  \n",
       "1   888300     54.24    54.59    55.22   53.89   4.9232    4.232  \n",
       "2   952400     54.38    54.08    54.85   53.93   4.9255    4.231  \n",
       "3  1213800     54.15    54.16    54.46   53.75   4.9239    4.238  \n",
       "4  1173900     53.90    52.80    54.20   52.25   4.9251    4.241  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_ori = pd.to_datetime(google.iloc[:, 0]).tolist()\n",
    "google.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094605</td>\n",
       "      <td>0.050227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021539</td>\n",
       "      <td>0.021539</td>\n",
       "      <td>0.092326</td>\n",
       "      <td>0.978389</td>\n",
       "      <td>0.938202</td>\n",
       "      <td>0.847145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033373</td>\n",
       "      <td>0.523804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018810</td>\n",
       "      <td>0.082769</td>\n",
       "      <td>0.082769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.972495</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.714279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.047461</td>\n",
       "      <td>0.026441</td>\n",
       "      <td>0.041238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.904495</td>\n",
       "      <td>0.931860</td>\n",
       "      <td>0.943359</td>\n",
       "      <td>0.027411</td>\n",
       "      <td>0.682536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023572</td>\n",
       "      <td>0.142825</td>\n",
       "      <td>0.106207</td>\n",
       "      <td>0.247630</td>\n",
       "      <td>0.247630</td>\n",
       "      <td>0.076062</td>\n",
       "      <td>0.954813</td>\n",
       "      <td>0.919476</td>\n",
       "      <td>0.860036</td>\n",
       "      <td>0.908203</td>\n",
       "      <td>0.008343</td>\n",
       "      <td>0.904755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200918</td>\n",
       "      <td>0.237416</td>\n",
       "      <td>0.224569</td>\n",
       "      <td>0.368600</td>\n",
       "      <td>0.368600</td>\n",
       "      <td>0.066738</td>\n",
       "      <td>0.905698</td>\n",
       "      <td>0.664794</td>\n",
       "      <td>0.812155</td>\n",
       "      <td>0.615234</td>\n",
       "      <td>0.022643</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  0.094605  0.050227  0.000000  0.021539  0.021539  0.092326  0.978389   \n",
       "1  0.000000  0.000000  0.018810  0.082769  0.082769  0.000000  0.972495   \n",
       "2  0.047461  0.026441  0.041238  0.000000  0.000000  0.014979  1.000000   \n",
       "3  0.023572  0.142825  0.106207  0.247630  0.247630  0.076062  0.954813   \n",
       "4  0.200918  0.237416  0.224569  0.368600  0.368600  0.066738  0.905698   \n",
       "\n",
       "         7         8         9         10        11  \n",
       "0  0.938202  0.847145  1.000000  0.033373  0.523804  \n",
       "1  1.000000  1.000000  0.935547  0.000000  0.714279  \n",
       "2  0.904495  0.931860  0.943359  0.027411  0.682536  \n",
       "3  0.919476  0.860036  0.908203  0.008343  0.904755  \n",
       "4  0.664794  0.812155  0.615234  0.022643  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax = MinMaxScaler().fit(google.iloc[:, 4].values.reshape((-1,1)))\n",
    "df_log = MinMaxScaler().fit_transform(google.iloc[:, 1:].astype('float32'))\n",
    "df_log = pd.DataFrame(df_log)\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 10 loss: 0.272533 time: 0.0006597042083740234\n",
      "epoch: 20 loss: 0.272347 time: 0.0007002353668212891\n",
      "epoch: 30 loss: 0.272032 time: 0.0006601810455322266\n",
      "epoch: 40 loss: 0.271498 time: 0.0006575584411621094\n",
      "epoch: 50 loss: 0.270591 time: 0.0006284713745117188\n",
      "epoch: 60 loss: 0.26905 time: 0.0006418228149414062\n",
      "epoch: 70 loss: 0.266411 time: 0.0006747245788574219\n",
      "epoch: 80 loss: 0.261816 time: 0.0007426738739013672\n",
      "epoch: 90 loss: 0.253563 time: 0.0006310939788818359\n",
      "epoch: 100 loss: 0.238662 time: 0.0006124973297119141\n"
     ]
    }
   ],
   "source": [
    "thought_vector = autoencoder.reducedimension(df_log.values, 4, 0.001, 128, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thought_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_layers = 1\n",
    "size_layer = 128\n",
    "timestamp = 5\n",
    "epoch = 500\n",
    "dropout_rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7ff23c502128>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "epoch: 100 avg loss: 0.226264208555\n",
      "epoch: 200 avg loss: 0.0964816752821\n",
      "epoch: 300 avg loss: 0.0767136435024\n",
      "epoch: 400 avg loss: 0.0496228779666\n",
      "epoch: 500 avg loss: 0.0471770029981\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "modelnn = model.Model(0.01, num_layers, thought_vector.shape[1], size_layer, 1, dropout_rate)\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for i in range(epoch):\n",
    "    init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
    "    total_loss = 0\n",
    "    for k in range(0, (thought_vector.shape[0] // timestamp) * timestamp, timestamp):\n",
    "        batch_x = np.expand_dims(thought_vector[k: k + timestamp, :], axis = 0)\n",
    "        batch_y = df_log.values[k + 1: k + timestamp + 1, 3].reshape([-1, 1])\n",
    "        last_state, _, loss = sess.run([modelnn.last_state, \n",
    "                                        modelnn.optimizer, \n",
    "                                        modelnn.cost], feed_dict={modelnn.X: batch_x, \n",
    "                                                                  modelnn.Y: batch_y, \n",
    "                                                                  modelnn.hidden_layer: init_value})\n",
    "        init_value = last_state\n",
    "        total_loss += loss\n",
    "    total_loss /= (thought_vector.shape[0] // timestamp)\n",
    "    if (i + 1) % 100 == 0:\n",
    "        print('epoch:', i + 1, 'avg loss:', total_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_predict = np.zeros(((thought_vector.shape[0] // timestamp) * timestamp, 1))\n",
    "init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
    "for k in range(0, (thought_vector.shape[0] // timestamp) * timestamp, timestamp):\n",
    "    out_logits, last_state = sess.run([modelnn.logits, modelnn.last_state], feed_dict = {modelnn.X:np.expand_dims(thought_vector[k: k + timestamp, :], axis = 0),\n",
    "                                     modelnn.hidden_layer: init_value})\n",
    "    init_value = last_state\n",
    "    output_predict[k: k + timestamp, :] = out_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error: 0.0510127100734\n"
     ]
    }
   ],
   "source": [
    "print('Mean Square Error:', np.mean(np.square(output_predict[:, 0] - df_log.iloc[1: (thought_vector.shape[0] // timestamp) * timestamp + 1, 0].values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import ARIMA model using stats model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-7.7935465732797873"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from itertools import product\n",
    "from scipy import stats\n",
    "    \n",
    "Qs = range(0, 1)\n",
    "qs = range(0, 2)\n",
    "Ps = range(0, 2)\n",
    "ps = range(0, 2)\n",
    "D=1\n",
    "parameters = product(ps, qs, Ps, Qs)\n",
    "parameters_list = list(parameters)\n",
    "best_aic = float(\"inf\")\n",
    "for param in parameters_list:\n",
    "    try:\n",
    "        arima=sm.tsa.statespace.SARIMAX(df_log.iloc[:,3].values, order=(param[0], D, param[1]), seasonal_order=(param[2], D, param[3], 1)).fit(disp=-1)\n",
    "    except:\n",
    "        continue\n",
    "    aic = arima.aic\n",
    "    if aic < best_aic and aic:\n",
    "        best_arima = arima\n",
    "        best_aic = aic\n",
    "        \n",
    "best_aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reverse_close(array):\n",
    "    return minmax.inverse_transform(array.reshape((-1,1))).reshape((-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WQKjTRnK8y5TzFzeWYmBoiOUQpDAnIiIiItJDre0+ymtayEqOxGrSkgAqfjJ0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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff202441d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_arima = best_arima.predict()\n",
    "x_range = np.arange(df_log.shape[0])\n",
    "fig = plt.figure(figsize = (15,6))\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(x_range, reverse_close(df_log.iloc[:,3].values), label = 'true Close')\n",
    "ax.plot(x_range, reverse_close(pred_arima), label = 'predict Close using Arima')\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])\n",
    "ax.legend(loc = 'upper center', bbox_to_anchor= (0.5, -0.05), fancybox = True, shadow = True, ncol = 5)\n",
    "plt.xticks(x_range[::5], date_ori[::5])\n",
    "plt.title('overlap market Close')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "boundary = (thought_vector.shape[0] // timestamp) * timestamp\n",
    "stack_predict = np.vstack([pred_arima[:boundary], output_predict.reshape((-1))]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "where_below_0 = np.where(stack_predict < 0)\n",
    "where_higher_1 = np.where(stack_predict > 1)\n",
    "stack_predict[where_below_0[0], where_below_0[1]] = 0\n",
    "stack_predict[where_higher_1[0], where_higher_1[1]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_df = pd.DataFrame(np.hstack([stack_predict, df_log.values[:boundary, 3].reshape((-1,1))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dhg/BgrR0NDQ0orDwMHbl7sWYMSPajA8KCsDo0YlIf3eZ0fGOHTvA19cH69ZuAQB8e0SF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23f578e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_df.corr(), annot= True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARIMA able to predict data that correlate 0.61 originally from original Close\n",
    "\n",
    "Deep Recurrent Neural Network able to predict data that correlate 0.48 originally from original Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,\n",
       "       learning_rate=0.05, max_delta_step=0, max_depth=7,\n",
       "       min_child_weight=1, missing=None, n_estimators=10000, nthread=-1,\n",
       "       objective='reg:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_xgd = {\n",
    "    'max_depth': 7,\n",
    "    'objective': 'reg:logistic',\n",
    "    'learning_rate': 0.05,\n",
    "    'n_estimators': 10000\n",
    "    }\n",
    "train_Y = df_log.values[:boundary, 3]\n",
    "clf = xgb.XGBRegressor(**params_xgd)\n",
    "clf.fit(stack_predict,train_Y, eval_set=[(stack_predict,train_Y)], \n",
    "        eval_metric='rmse', early_stopping_rounds=20, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stacked = clf.predict(stack_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EMWnSoyxYMJdevUIqvL5VqwDat+/Irl07Km2/TZu2/P77zjrHN3To3XTr1p0D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0Ru9ALqhhpps4b/4JbRsNtcVJ3rIEqnZnH+zFflrqWwKJPIKBXnT7RiNTtndW24ZpJlojbQP1c/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\nGIIkmBNCCCGEEEKIIej/B/owcfU06XGRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1e0fb2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "x_range = np.arange(boundary)\n",
    "plt.plot(x_range, reverse_close(train_Y), label = 'Real Close')\n",
    "plt.plot(x_range, reverse_close(pred_arima[:boundary]), label = 'ARIMA Close')\n",
    "plt.plot(x_range, reverse_close(output_predict), label = 'RNN Close')\n",
    "plt.plot(x_range, reverse_close(stacked), label = 'Stacked Close')\n",
    "plt.legend()\n",
    "plt.xticks(x_range[::5], date_ori[:boundary][::5])\n",
    "plt.title('stacked RNN + ARIMA with XGB')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pretty insane i can say!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QvHlz9OzZE9nZ2SgtLYVerwfwvx6AG/1dvnwZwI0loLKyMjg7O9fL/g4fPoyU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\n7oEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2000e1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "plot_importance(clf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arima is more important than RNN"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
